PROJECT SUMMARY Data from our previous funding period and supporting evidence from other INIA-Neuroimmune (INIA-N) laboratories provide the foundation for the overall hypothesis of this proposal: Behavioral changes characteristic of excessive alcohol consumption and alcohol use disorder (AUD) are due, in part, to neuroimmune mechanisms that significantly reshape the transcriptome. Drug repurposing algorithms can analyze cross-species transcriptome changes to predict compounds that reduce alcohol consumption. We propose that brain dysfunction in alcoholics is the result of dysregulation in both the protein coding and non-coding transcriptome. Advances in genomic studies by our research team and INIA-N collaborators have enabled us to generate transcriptome data on an unprecedented scale. Devising methods to visualize and analyze these data to produce meaningful biological interpretation is one of the next important steps. We aim to take full advantage of the emerging technologies, allowing us to move forward in the post- genomic, ?big data? era. We previously used RNA sequencing (RNA-Seq) to define the transcriptome in four brain regions of 90 human cases (alcoholics and matched controls). In addition to identifying novel splice variants and co-variation with lifetime alcohol consumption, we identified novel changes in long non-coding RNAs (lncRNAs) in human brain and propose that these play an important role in gene expression changes. Differentially expressed lncRNAs that are correlated with alcohol consumption and have syntenic conservation between humans and mice will be prioritized for genetic and behavioral testing in mice in collaboration with INIA-N investigators. Other interactions involve mining the RNA-Seq transcriptome profiles in human subjects and macaque to link expression changes with genetic differences found in the Collaborative Studies on Genetics of Alcoholism (COGA). This will allow us to define, in unprecedented detail, changes in RNA splicing and pathways constructed from detected single-nucleotide polymorphisms. Finally, we will study the convergence of transcriptome changes in human, macaque, and rodent brain to determine which rodent models reflect the gene expression changes seen in human AUD. The overlapping changes in expression and associated networks produced by excessive alcohol consumption can be used to predict drugs that will normalize the network using cutting-edge computational analyses. Thus, we and INIA-N collaborators will utilize our combined transcriptome data to predict novel therapeutics based on cross-species convergence of gene networks and then test FDA-approved drugs predicted to target the affected networks in mouse and rat models of excessive alcohol consumption. The most promising drugs will then be selected for testing in human alcoholics. These efforts integrate advanced computational, genomic, molecular, functional system, and behavioral studies to be integrated into a systematic framework designed to advance treatment strategies for AUD.